CN110110437B - Automobile high-frequency noise prediction method based on related interval uncertainty theory - Google Patents
Automobile high-frequency noise prediction method based on related interval uncertainty theory Download PDFInfo
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- G—PHYSICS
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
- G01H11/06—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
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Abstract
The invention discloses an automobile high-frequency noise prediction method based on a related interval uncertainty theory, which is characterized in that on the basis of establishing an overall automobile statistical energy model, defining an acoustic package and comparing the model, geometric and material parameters with poor consistency in engineering are selected as interval uncertainty parameters, and the relation between the uncertainty parameters is described through a linear constraint inequality equation; calculating the local sensitivity of the noise in the vehicle to each uncertain parameter, reordering the uncertain parameters according to the sensitivity, and taking the uncertain parameters into an inequality equation to obtain ordered uncertain parameter vectors and uncertain parameter correction perturbation intervals; and carrying out first-order Taylor series expansion on the noise in the vehicle, carrying in an uncertain parameter central value and correcting a perturbation interval, and obtaining the distribution limit of the high-frequency noise in the vehicle. The invention provides a related interval uncertainty theory for the first time, describes the relation between uncertain parameters through a linear inequality equation, and has important values in science and engineering.
Description
Technical Field
The invention relates to the field of automobile NVH (noise, vibration and harshness) performance development, in particular to an automobile high-frequency noise prediction method based on a related interval uncertainty theory.
Background
With advances in science and technology and increased consumption levels, users have increasingly stringent requirements on automobile NVH performance. The NVH performance of the automobile is one of important indexes for distinguishing the quality of the automobile, and the quality of the NVH performance directly influences the evaluation of the automobile performance by a user. Therefore, the method effectively analyzes and predicts the noise in the automobile design process, becomes an essential step in the automobile design and development process, and can effectively shorten the automobile development period, save the development cost and improve the NVH level of the automobile.
Automobile noise can be divided into two major components, namely low frequency noise and high frequency noise. Among them, automobile high frequency noise is the most important NVH problem for automobiles, especially electric automobiles. The analysis and prediction of the high-frequency noise of the automobile mainly adopts a statistical energy method. However, due to limitations of manufacturing errors, assembly errors, and use conditions, uncertainty is unavoidable in the prediction process of high-frequency noise of automobiles. These uncertainties may lead to a series of problems such as difficulty in scaling the simulation model, poor consistency of vehicle performance, and insignificant effects of the noise optimization scheme.
The uncertain parameters in the statistical energy model of the automobile include acoustic coverage, material density, structural dimensions, etc. These uncertainty parameters are not completely independent of each other, and there is a limiting relationship between the uncertainty parameters. The traditional probability uncertainty model and interval uncertainty model can not describe the limiting relation, and difficulty is caused to the prediction of the noise in the vehicle under the uncertain condition.
Therefore, the prediction method of the automobile high-frequency noise under the uncertain conditions is provided, and the interrelation among uncertain parameters is considered, so that the prediction method has very important scientific and engineering significance.
Disclosure of Invention
The invention provides an automobile high-frequency noise prediction method based on a correlation interval uncertainty theory, which provides the correlation interval uncertainty theory, solves the problem of relation description among uncertain parameters, and considers the relation among the uncertain parameters for the first time in the field of automobile high-frequency noise prediction under uncertain conditions. The method can effectively predict the high-frequency noise and the fluctuation level thereof in the vehicle.
In order to solve the technical problems, the invention adopts the following technical scheme:
dividing the whole vehicle into a plurality of subsystems based on an automobile geometric model and a statistical energy theory, selecting nodes capable of describing shape characteristics of the subsystems, simplifying a subsystem model, thereby establishing the whole vehicle statistical energy model, and endowing a response structure and material parameters;
obtaining an acoustic package coverage rate parameter through a geometric model, obtaining an acoustic package material parameter through a test, and endowing the obtained parameter into a whole vehicle statistical energy model to finish the definition of an acoustic package;
measuring sound load curves of all positions outside the automobile under the common working conditions of the automobile, loading the sound loads into a whole automobile statistical energy model in a constraint mode, calculating high-frequency noise in the automobile under all working conditions, and comparing the high-frequency noise with test results;
step four, selecting geometry and materials with poor consistency in engineeringThe material parameter is used as an uncertain parameter, an uncertain parameter vector b is established, each uncertain parameter is described by adopting an interval model, and the central value b of the uncertain parameter is determined according to the existing data, references and engineering experience c A section radius Δb;
fifthly, establishing an uncertainty parameter linear constraint inequality equation according to the correlation among the uncertainty parametersWherein m is the number of uncertain parameters, a i For uncertain equation coefficients;
step six, analyzing local sensitivity of noise in the vehicle to each uncertain parameter by adopting a numerical sensitivity analysis method
Step seven, expanding the noise in the vehicle into a first-order Taylor series form of the interval uncertainty parameterCalculating noise center value in vehicle>
Step eight, sorting the local sensitivity from large to small to obtain an ordered uncertain parameter vector b', and takingCarry-in inequality equation->If inequality constraint is satisfied, further takeUntil (I)>When the inequality is not satisfiedBeam requirements;
step nine, taking uncertain parameters to correct perturbation intervalsAnd when i > n, Δb i ＝0；
Step ten, Δb' 1 ，Δb′ 1 ，…，Δb′ n-1 ,Δb″ n ,Δb″ n+1 ,…，Δb″ m Solving the perturbation radius of in-car noise in Taylor series form of brought noiseThereby realizing the prediction of the high-frequency noise in the vehicle.
The invention has the following advantages and beneficial effects:
(1) The method considers the relation between uncertain parameters for the first time in the field of vehicle NVH uncertainty development, and describes the relation through a linear inequality equation between uncertain parameters;
(2) The method provides a related interval uncertainty theory, and solves the problem of analysis of an uncertainty model related to parameters on the basis of an interval perturbation method;
(3) The method establishes a complete in-vehicle high-frequency noise uncertainty analysis flow, gives consideration to calculation accuracy and calculation efficiency, and has important values in science and engineering.
Drawings
FIG. 1 is a schematic diagram of an automobile high-frequency noise prediction method based on a correlation interval uncertainty theory;
FIG. 2 is a schematic diagram of a structural subsystem of a whole vehicle statistical energy model of a sample vehicle;
FIG. 3 is a schematic diagram of a sound cavity subsystem of a whole vehicle statistical energy model of a sample vehicle;
FIG. 4 is a graph of the results of the test of the acoustic absorption coefficient of the sound package of a sample car;
FIG. 5 is a graph of the results of a sample car acoustic packet transmission loss test;
FIG. 6 is a schematic diagram of a sample car acoustic load test microphone arrangement;
FIG. 7 is a graph comparing the calculation result and the actual measurement result of the high-frequency noise of the whole vehicle;
FIG. 8a is a graph of the analysis result of the flow resistance sensitivity of the noise in the vehicle to the uncertainty parameter; FIG. 8b is a graph of the result of the in-vehicle noise versus uncertainty parameter thickness sensitivity analysis;
fig. 9 is a graph showing the result of the in-vehicle high-frequency noise section limit analysis.
Detailed Description
The subject of this example was an electric sport utility vehicle with front and rear motors, four wheel drive, maximum power 355 horsepower, peak torsional 580 nm.
The first step: and establishing a whole vehicle statistical energy model of the vehicle type. The whole vehicle statistical energy model comprises a structural model (shown in figure 2) consisting of 1172 panel subsystems and an acoustic cavity model (shown in figure 3) consisting of 80 acoustic cavity subsystems. And each subsystem is connected, including between the plate and the plate, between the plate and the acoustic cavity, and between the acoustic cavity and the acoustic cavity, so as to ensure the energy transfer between the subsystems. And according to the structural attribute of the whole vehicle plate, the plate is endowed with the corresponding physical attribute.
And a second step of: the sound absorption characteristics of the acoustic package parts are effectively tested by adopting a sound absorption measuring method of the reverberation room, firstly, the reverberation time of the reverberation room is measured, then the sound absorption materials or parts are put in, the reverberation time is measured, and the sound absorption quantity A of a test piece is calculated according to a racing bine formula by using the two reverberation times t For test pieces with uniformly covered surfaces, the sound absorption coefficient α t The calculation formula is as follows:
wherein S is t Is the area of the test piece. Acoustic packaging material for main car body parts such as fire wall, floor, luggage floor and the like, has a volume of 9m 3 Sound absorption tests were performed in the reverberant room of (1), wherein the test pieces had a size of 1 x 1.2m, and the test results are shown in fig. 4.
Sound insulation performance tests of the components were carried out in a test environment consisting of connected reverberatory chambers, anechoic chambers, using the sound intensity method according to standard ISO 15186-1:2000. In the test process, a non-directional sound source is placed at the right side wall corner of a window of a reverberation chamber (sound source chamber), and 4 sound pressure sensors are placed at a distance of 1 meter from the window to measure the average sound pressure level of the reverberation chamber. The sound intensity probe is arranged in the anechoic chamber to measure the average normal sound intensity level of the measuring surface of the test piece, and the emphasis is that each measuring point in the anechoic chamber is distributed on an imaginary hemispherical envelope surface covering the test sample piece. The transmission loss expression is
Wherein L is p1 Is the average sound pressure level measured in the sound source chamber; l (L) In Is the average normal sound intensity level of the measurement surface in the receiving chamber; s is S m The total area of the faces, the area of the test piece in the S test, was measured. The sound insulation test is carried out on the metal plates of main vehicle body parts such as fire wall, floor, luggage floor, front vehicle door, rear vehicle door and the like and the attached sound package materials, and the sound insulation test result of the main parts is shown in figure 5.
And a third step of: and carrying out a whole vehicle acoustic load test on the researched sample vehicle to obtain the vehicle body outside acoustic excitation distribution and the in-vehicle noise response under different working conditions. And (3) carrying out acoustic load test on the drum of the whole vehicle semi-anechoic chamber, wherein a two-drive drum is adopted, and a standard noise pavement is selected on the surface of the drum. And through the data acquisition front end and the microphones, the sound pressure level of the microphones at the positions of each measuring point of the vehicle under the working conditions of idling, acceleration and uniform speed is recorded respectively, and the microphones are arranged in the process of sound load test (shown in figure 6). And (3) carrying out energy averaging on the microphone test data in the same acoustic cavity, wherein the calculated data is acoustic load data corresponding to the position of the acoustic cavity. And loading the acoustic load test result into a whole vehicle statistical energy model, and comparing the simulated driver's ear noise with the actual measurement noise, wherein the result is shown in figure 7, and the accuracy of the simulation model is verified.
Fourth step: according to the analysis result of the high-frequency noise contribution quantity in the vehicle, the acoustic package parameters of the inner front wall have larger influence on the whole vehicle noise,the inner front perimeter acoustic packet parameters are therefore chosen as uncertainty variables. The inner front periphery of the research vehicle model adopts a form of hard layer and soft layer, wherein the hard layer is made of EVA, and the soft layer is made of PU foam. The parameters of the acoustic package of the inner front wall are numerous, the density, the flow resistance and the thickness of the hard layer EVA with larger influence on the sound absorption and insulation performance of the inner front wall are selected according to engineering experience, the density, the flow resistance and the thickness of the soft layer PU, and 10 parameters with different acoustic package coverage rates and different thicknesses are taken as uncertain variables, b is respectively used for 1 To b 10 Representative of the group. The center value and the section radius of the parameter are determined according to the actual measurement data and the manufacturing accuracy range of the supplier, as shown in table 1.
Fifth step: according to engineering practical conditions, the sum of the inner front wall coverage rates of different thicknesses is less than 100%, thereby establishing an uncertainty parameter linear constraint inequality equation
b 7 +b 8 +b 9 +b 10 ≤100 (3)
Sixth step: analyzing the sensitivity of the noise in the vehicle to each uncertain parameter, setting x and g (x) to respectively represent the acoustic package system parameter and the noise response in the vehicle, and defining the numerical sensitivity of g (x) to x as
Where Δx is a small increment of x.
The sensitivity of 10 uncertain parameters of the inner front periphery is calculated by taking the head noise response of a driver as an index for evaluating the noise in the vehicleThe sensitivity analysis results are shown in FIG. 8.
Seventh step: and calculating uncertainty of noise response in the vehicle by adopting an interval perturbation method. Let b denote the interval uncertainty variable vector, b c The interval variable intermediate value vector is represented, Δb represents an interval variable interval radius vector, and b represent interval variable upper bound vectors and lower bound vectors, respectively. Then there are:
b＝[b 1 ,b 2 ,b 3 ,b 4 ,b 5 ,b 6 ,b 7 ,b 8 ,b 9 ,b 10 ] (5)
let F (b, F) denote the in-vehicle driver's ear noise response, where F denotes the vibration frequency and in-vehicle noise is a function of an uncertainty variable and also varies with frequency. Expansion of F (b, F) using a first-order Taylor series
Wherein F is C (f) And Δf (F) represents the center value and the section radius of the driver's earring noise response at each vibration frequency.
Will b c The value of (F) is brought into a whole vehicle statistical energy model to obtain F C (f)。
Eighth step: sequencing the local sensitivity from large to small to obtain a new uncertain parameter sequence b', and takingCarry-over inequality equation b 7 +b 8 +b 9 +b 10 In less than or equal to 100, if the inequality constraint is satisfied, further takingUntil (I)>When the inequality constraint requirement is not satisfied. In the sample vehicle under investigation, the parameter b is not determined 1 To b 6 Does not contribute to the inequality constraint, so the first 6 uncertain parameters are ordered unchanged, i.e. b' j ＝b j (j=1, 2, …, 6). At each frequency, pair b 7 To b 10 Reordered into
b′ 7 ＝b 10 ,b′ 8 ＝b 9 ,b′ 9 ＝b 7 ,b′ 10 ＝b 8 (7)
Bringing it into the inequality equation, it was found whenWhen the inequality requirement is not satisfied.
Ninth step: taking outTo obtain Deltab 10 ＝1。
Step ten, Δb' 1 ，Δb′ 2 ，…，Δb′ 9 ,Δb″ 10 Is carried into a part (6) of the container,the sensitivity of the noise response to the j-th uncertainty is expressed, and the result is obtained. Upper and lower bound of noise response-> FThe calculation can be performed according to the central value and the interval radius, and the calculation result is shown in figure 9.
Table 1 uncertainty of the center value of the variable and the interval radius
The foregoing describes one embodiment of the invention in detail, but the description is only a preferred embodiment of the invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications made in accordance with the scope of the present invention shall fall within the scope of the patent covered by this invention.
Claims (1)
1. The automobile high-frequency noise prediction method based on the related interval uncertainty theory is characterized by comprising the following steps of:
the first step: dividing the whole vehicle into a plurality of subsystems based on a geometric model of the vehicle and a statistical energy theory, selecting nodes capable of describing shape characteristics of the subsystems, simplifying the subsystem model, thereby establishing a statistical energy model of the whole vehicle, and endowing a response structure and material parameters;
and a second step of: obtaining an acoustic package coverage rate parameter through a geometric model, obtaining an acoustic package material parameter through a test, and endowing the obtained parameter into a whole vehicle statistical energy model to complete the definition of an acoustic package;
and a third step of: measuring sound load curves of all positions outside the automobile under common working conditions of the automobile, loading the sound loads into a whole automobile statistical energy model in a constraint mode, calculating high-frequency noise in the automobile under all working conditions, and comparing the high-frequency noise with test results;
fourth step: geometry and material parameters with poor consistency in engineering are selected as uncertain parameters, an uncertain parameter vector b is established, each uncertain parameter is described by adopting an interval model, and a central value b of each uncertain parameter is determined according to the existing data, references and engineering experience c A section radius Δb;
fifth step: establishing an uncertainty parameter linear constraint inequality equation according to the interrelation between uncertainty parametersWherein m is the number of uncertain parameters, a i For uncertain equation coefficients;
sixth step: analyzing local sensitivity of noise in the vehicle to each uncertain parameter by adopting a numerical sensitivity analysis method
Seventh step: first-order Taylor series form for expanding noise in vehicle into interval uncertainty parameterCalculating noise center value in vehicle>
Eighth step: ordering the local sensitivity from big to small to obtain ordered uncertain parameter vector b', takingCarry-in inequality equation->If inequality constraint is satisfied, further takeUntil (I)>When the inequality constraint requirement is not met;
ninth step: correction of perturbation intervals by taking uncertain parametersAnd when i > n, Δb i ″＝0；
Tenth step: will Δb 1 ′，Δb 1 ′，…，Δb′ n-1 ,Δb′ n ′,Δb′ n ′ +1 ,…，Δb′ m Solving the perturbation radius of the noise in the vehicle in Taylor series form of the' carried-in noiseThereby realizing the prediction of the high-frequency noise in the vehicle.
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| CN111753368B (en) * | 2020-05-18 | 2022-07-08 | 重庆长安汽车股份有限公司 | Method for predicting sound absorption performance in vehicle |
| CN111625904B (en) * | 2020-05-29 | 2023-08-04 | 中国航空工业集团公司西安飞机设计研究所 | Low-frequency simulation method for noise in propeller aircraft cabin |
| CN111625905B (en) * | 2020-05-29 | 2023-08-04 | 中国航空工业集团公司西安飞机设计研究所 | High-frequency simulation method for noise in cabin of propeller aircraft |
| CN114720150B (en) * | 2022-03-30 | 2023-09-12 | 襄阳达安汽车检测中心有限公司 | Test method and test system for sound insulation performance of whole vehicle |
| CN115238528B (en) * | 2022-09-23 | 2022-12-16 | 北京科技大学 | Aircraft structure dynamics parameter sensitivity analysis method based on interval similarity |
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